I am given a data set of 100k instances and I am being told that it belongs to one of four statistical distributions (normal, truncated normal, poisson and uniform). I am wondering how I may go about finding which stastical model best represents this dataset perhaps using numpy?
What I am thinking is to literally count occurrences of every instance and then divide over size giving the probability of each instance, and then attempting to find that probability using different statistical models such as P(X) = 1/b-a for uniform and see which one it best matches? I feel like this is very tedious and will not work.
Can anyone guide me in the right direction?